The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

esults. We demonstrate that the nonlinear methods bring an improvement, although rather
marginal, to the sub-optimal linear methods, such as the linear regression of predictor’s
principal components. <strong>The</strong> best linear method, multiple linear regression of gridpoint values, is
however not surpassed by any nonlinear method. <strong>The</strong> performance of the best nonlinear
method, ANN with gridpoint values as predictors, is fairly close to the linear method, but in
general is slightly worse. <strong>The</strong> classification does not improve the fit between the downscaled
and observed values either. In addition to the correspondence measures, we evaluate the
downscaled temperature in terms of temporal and spatial autocorrelations and statistical
distributions. ANNs are the only method capable of reproducing at least some deviations of the
statistical distributions from normality. Spatial autocorrelations are best reproduced by the
classification-based methods, whereas the temporal autocorrelations are best captured by the
linear methods. Taken together, the linear regression of gridpoint values appears to be the
method whose performance is closest to the optimum.
Detection and simulation of extreme rainfall in a topographically complex region
Speaker: Chris Lennard
Chris Lennard
University of Cape Town
lennard@egs.uct.ac.za
Annually, extreme rainfall causes millions of dollars worth of damage and regularly
displaces thousands of people in Cape Town, South Africa. Forecast skill for extreme
precipitation in the region is generally poor thus an accurate, qualitative forecast is necessary.
An empirical technique is presented for the identification of weather systems associated with
extreme rainfall as are results from high resolution simulations of these storms.
Regression Procedures and <strong>The</strong>ir Applications to Climate Change Impact Analyses
Speaker: Qian Li
Qian Li
Meteorological Service of Canada Branch, Environment Canada
qian.li@ec.gc.ca
Chad Shouquan Cheng
Meteorological Service of Canada Branch, Environment Canada
Guilong Li
Meteorological Service of Canada Branch, Environment Canada
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